Tag Archives: brain-like computing

From the memristor to the atomristor?

I’m going to let Michael Berger explain the memristor (from Berger’s Jan. 2, 2017 Nanowerk Spotlight article),

In trying to bring brain-like (neuromorphic) computing closer to reality, researchers have been working on the development of memory resistors, or memristors, which are resistors in a circuit that ‘remember’ their state even if you lose power.

Today, most computers use random access memory (RAM), which moves very quickly as a user works but does not retain unsaved data if power is lost. Flash drives, on the other hand, store information when they are not powered but work much slower. Memristors could provide a memory that is the best of both worlds: fast and reliable.

He goes on to discuss a team at the University of Texas at Austin’s work on creating an extraordinarily thin memristor: an atomristor,

he team’s work features the thinnest memory devices and it appears to be a universal effect available in all semiconducting 2D monolayers.

The scientists explain that the unexpected discovery of nonvolatile resistance switching (NVRS) in monolayer transitional metal dichalcogenides (MoS2, MoSe2, WS2, WSe2) is likely due to the inherent layered crystalline nature that produces sharp interfaces and clean tunnel barriers. This prevents excessive leakage and affords stable phenomenon so that NVRS can be used for existing memory and computing applications.

“Our work opens up a new field of research in exploiting defects at the atomic scale, and can advance existing applications such as future generation high density storage, and 3D cross-bar networks for neuromorphic memory computing,” notes Akinwande [Deji Akinwande, an Associate Professor at the University of Texas at Austin]. “We also discovered a completely new application, which is non-volatile switching for radio-frequency (RF) communication systems. This is a rapidly emerging field because of the massive growth in wireless technologies and the need for very low-power switches. Our devices consume no static power, an important feature for battery life in mobile communication systems.”

Here’s a link to and a citation for the Akinwande team’s paper,

Atomristor: Nonvolatile Resistance Switching in Atomic Sheets of Transition Metal Dichalcogenides by Ruijing Ge, Xiaohan Wu, Myungsoo Kim, Jianping Shi, Sushant Sonde, Li Tao, Yanfeng Zhang, Jack C. Lee, and Deji Akinwande. Nano Lett., Article ASAP DOI: 10.1021/acs.nanolett.7b04342 Publication Date (Web): December 13, 2017

Copyright © 2017 American Chemical Society

This paper appears to be open access.

ETA January 23, 2018: There’s another account of the atomristor in Samuel K. Moore’s January 23, 2018 posting on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website).

Neuristors and brainlike computing

As you might suspect, a neuristor is based on a memristor .(For a description of a memristor there’s this Wikipedia entry and you can search this blog with the tags ‘memristor’ and neuromorphic engineering’ for more here.)

Being new to neuristors ,I needed a little more information before reading the latest and found this Dec. 24, 2012 article by John Timmer for Ars Technica (Note: Links have been removed),

Computing hardware is composed of a series of binary switches; they’re either on or off. The other piece of computational hardware we’re familiar with, the brain, doesn’t work anything like that. Rather than being on or off, individual neurons exhibit brief spikes of activity, and encode information in the pattern and timing of these spikes. The differences between the two have made it difficult to model neurons using computer hardware. In fact, the recent, successful generation of a flexible neural system required that each neuron be modeled separately in software in order to get the sort of spiking behavior real neurons display.

But researchers may have figured out a way to create a chip that spikes. The people at HP labs who have been working on memristors have figured out a combination of memristors and capacitors that can create a spiking output pattern. Although these spikes appear to be more regular than the ones produced by actual neurons, it might be possible to create versions that are a bit more variable than this one. And, more significantly, it should be possible to fabricate them in large numbers, possibly right on a silicon chip.

The key to making the devices is something called a Mott insulator. These are materials that would normally be able to conduct electricity, but are unable to because of interactions among their electrons. Critically, these interactions weaken with elevated temperatures. So, by heating a Mott insulator, it’s possible to turn it into a conductor. In the case of the material used here, NbO2, the heat is supplied by resistance itself. By applying a voltage to the NbO2 in the device, it becomes a resistor, heats up, and, when it reaches a critical temperature, turns into a conductor, allowing current to flow through. But, given the chance to cool off, the device will return to its resistive state. Formally, this behavior is described as a memristor.

To get the sort of spiking behavior seen in a neuron, the authors turned to a simplified model of neurons based on the proteins that allow them to transmit electrical signals. When a neuron fires, sodium channels open, allowing ions to rush into a nerve cell, and changing the relative charges inside and outside its membrane. In response to these changes, potassium channels then open, allowing different ions out, and restoring the charge balance. That shuts the whole thing down, and allows various pumps to start restoring the initial ion balance.

Here’s a link to and a citation for the research paper described in Timmer’s article,

A scalable neuristor built with Mott memristors by Matthew D. Pickett, Gilberto Medeiros-Ribeiro, & R. Stanley Williams. Nature Materials 12, 114–117 (2013) doi:10.1038/nmat3510 Published online 16 December 2012

This paper is behind a paywall.

A July 28, 2017 news item on Nanowerk provides an update on neuristors,

A future android brain like that of Star Trek’s Commander Data might contain neuristors, multi-circuit components that emulate the firings of human neurons.

Neuristors already exist today in labs, in small quantities, and to fuel the quest to boost neuristors’ power and numbers for practical use in brain-like computing, the U.S. Department of Defense has awarded a $7.1 million grant to a research team led by the Georgia Institute of Technology. The researchers will mainly work on new metal oxide materials that buzz electronically at the nanoscale to emulate the way human neural networks buzz with electric potential on a cellular level.

A July 28, 2017 Georgia Tech news release, which originated the news item, delves further into neuristors and the proposed work leading to an artificial retina that can learn (!). This was not where I was expecting things to go,

But let’s walk expectations back from the distant sci-fi future into the scientific present: The research team is developing its neuristor materials to build an intelligent light sensor, and not some artificial version of the human brain, which would require hundreds of trillions of circuits.

“We’re not going to reach circuit complexities of that magnitude, not even a tenth,” said Alan Doolittle, a professor at Georgia Tech’s School of Electrical and Computer Engineering. “Also, currently science doesn’t really know yet very well how the human brain works, so we can’t duplicate it.”

Intelligent retina

But an artificial retina that can learn autonomously appears well within reach of the research team from Georgia Tech and Binghamton University. Despite the term “retina,” the development is not intended as a medical implant, but it could be used in advanced image recognition cameras for national defense and police work.

At the same time, it would significantly advance brain-mimicking, or neuromorphic, computing. The research field that takes its cues from what science already does know about how the brain computes to develop exponentially more powerful computing.

The retina would be comprised of an array of ultra-compact circuits called neuristors (a word combining “neuron” and “transistor”) that sense light, compute an image out of it and store the image. All three of the functions would occur simultaneously and nearly instantaneously.

“The same device senses, computes and stores the image,” Doolittle said. “The device is the sensor, and it’s the processor, and it’s the memory all at the same time.” A neuristor itself is comprised in part of devices called memristors inspired by the way human neurons work.

Brain vs. PC

That cuts out loads of processing and memory lag time that are inherent in traditional computing.

Take the device you’re reading this article on: Its microprocessor has to tap a separate memory component to get data, then do some processing, tap memory again for more data, process some more, etc. “That back-and-forth from memory to microprocessor has created a bottleneck,” Doolittle said.

A neuristor array breaks the bottleneck by emulating the extreme flexibility of biological nervous systems: When a brain computes, it uses a broad set of neural pathways that flash with enormous data. Then, later, to compute the same thing again, it will use quite different neural paths.

Traditional computer pathways, by contrast, are hardwired. For example, look at a present-day processor and you’ll see lines etched into it. Those are pathways that computational signals are limited to.

The new memristor materials at the heart of the neuristor are not etched, and signals flow through the surface very freely, more like they do through the brain, exponentially increasing the number of possible pathways computation can take. That helps the new intelligent retina compute powerfully and swiftly.

Terrorists, missing children

The retina’s memory could also store thousands of photos, allowing it to immediately match up what it sees with the saved images. The retina could pinpoint known terror suspects in a crowd, find missing children, or identify enemy aircraft virtually instantaneously, without having to trawl databases to correctly identify what is in the images.

Even if you take away the optics, the new neuristor arrays still advance artificial intelligence. Instead of light, a surface of neuristors could absorb massive data streams at once, compute them, store them, and compare them to patterns of other data, immediately. It could even autonomously learn to extrapolate further information, like calculating the third dimension out of data from two dimensions.

“It will work with anything that has a repetitive pattern like radar signatures, for example,” Doolittle said. “Right now, that’s too challenging to compute, because radar information is flying out at such a high data rate that no computer can even think about keeping up.”

Smart materials

The research project’s title acronym CEREBRAL may hint at distant dreams of an artificial brain, but what it stands for spells out the present goal in neuromorphic computing: Cross-disciplinary Electronic-ionic Research Enabling Biologically Realistic Autonomous Learning.

The intelligent retina’s neuristors are based on novel metal oxide nanotechnology materials, unique to Georgia Tech. They allow computing signals to flow flexibly across pathways that are electronic, which is customary in computing, and at the same time make use of ion motion, which is more commonly know from the way batteries and biological systems work.

The new materials have already been created, and they work, but the researchers don’t yet fully understand why.

Much of the project is dedicated to examining quantum states in the materials and how those states help create useful electronic-ionic properties. Researchers will view them by bombarding the metal oxides with extremely bright x-ray photons at the recently constructed National Synchrotron Light Source II.

Grant sub-awardee Binghamton University is located close by, and Binghamton physicists will run experiments and hone them via theoretical modeling.

‘Sea of lithium’

The neuristors are created mainly by the way the metal oxide materials are grown in the lab, which has advantages over building neuristors in a more wired way.

This materials-growing approach is conducive to mass production. Also, though neuristors in general free signals to take multiple pathways, Georgia Tech’s neuristors do it much more flexibly thanks to chemical properties.

“We also have a sea of lithium, and it’s like an infinite reservoir of computational ionic fluid,” Doolittle said. The lithium niobite imitates the way ionic fluid bathes biological neurons and allows them to flash with electric potential while signaling. In a neuristor array, the lithium niobite helps computational signaling move in myriad directions.

“It’s not like the typical semiconductor material, where you etch a line, and only that line has the computational material,” Doolittle said.

Commander Data’s brain?

“Unlike any other previous neuristors, our neuristors will adapt themselves in their computational-electronic pulsing on the fly, which makes them more like a neurological system,” Doolittle said. “They mimic biology in that we have ion drift across the material to create the memristors (the memory part of neuristors).”

Brains are far superior to computers at most things, but not all. Brains recognize objects and do motor tasks much better. But computers are much better at arithmetic and data processing.

Neuristor arrays can meld both types of computing, making them biological and algorithmic at once, a bit like Commander Data’s brain.

The research is being funded through the U.S. Department of Defense’s Multidisciplinary University Research Initiatives (MURI) Program under grant number FOA: N00014-16-R-FO05. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of those agencies.

Fascinating, non?

A more complex memristor: from two terminals to three for brain-like computing

Researchers have developed a more complex memristor device than has been the case according to an April 6, 2015 Northwestern University news release (also on EurekAlert),

Researchers are always searching for improved technologies, but the most efficient computer possible already exists. It can learn and adapt without needing to be programmed or updated. It has nearly limitless memory, is difficult to crash, and works at extremely fast speeds. It’s not a Mac or a PC; it’s the human brain. And scientists around the world want to mimic its abilities.

Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons.

“Computers are very impressive in many ways, but they’re not equal to the mind,” said Mark Hersam, the Bette and Neison Harris Chair in Teaching Excellence in Northwestern University’s McCormick School of Engineering. “Neurons can achieve very complicated computation with very low power consumption compared to a digital computer.”

A team of Northwestern researchers, including Hersam, has accomplished a new step forward in electronics that could bring brain-like computing closer to reality. The team’s work advances memory resistors, or “memristors,” which are resistors in a circuit that “remember” how much current has flowed through them.

“Memristors could be used as a memory element in an integrated circuit or computer,” Hersam said. “Unlike other memories that exist today in modern electronics, memristors are stable and remember their state even if you lose power.”

Current computers use random access memory (RAM), which moves very quickly as a user works but does not retain unsaved data if power is lost. Flash drives, on the other hand, store information when they are not powered but work much slower. Memristors could provide a memory that is the best of both worlds: fast and reliable. But there’s a problem: memristors are two-terminal electronic devices, which can only control one voltage channel. Hersam wanted to transform it into a three-terminal device, allowing it to be used in more complex electronic circuits and systems.

The memristor is of some interest to a number of other parties prominent amongst them, the University of Michigan’s Professor Wei Lu and HP (Hewlett Packard) Labs, both of whom are mentioned in one of my more recent memristor pieces, a June 26, 2014 post.

Getting back to Northwestern,

Hersam and his team met this challenge by using single-layer molybdenum disulfide (MoS2), an atomically thin, two-dimensional nanomaterial semiconductor. Much like the way fibers are arranged in wood, atoms are arranged in a certain direction–called “grains”–within a material. The sheet of MoS2 that Hersam used has a well-defined grain boundary, which is the interface where two different grains come together.

“Because the atoms are not in the same orientation, there are unsatisfied chemical bonds at that interface,” Hersam explained. “These grain boundaries influence the flow of current, so they can serve as a means of tuning resistance.”

When a large electric field is applied, the grain boundary literally moves, causing a change in resistance. By using MoS2 with this grain boundary defect instead of the typical metal-oxide-metal memristor structure, the team presented a novel three-terminal memristive device that is widely tunable with a gate electrode.

“With a memristor that can be tuned with a third electrode, we have the possibility to realize a function you could not previously achieve,” Hersam said. “A three-terminal memristor has been proposed as a means of realizing brain-like computing. We are now actively exploring this possibility in the laboratory.”

Here’s a link to and a citation for the paper,

Gate-tunable memristive phenomena mediated by grain boundaries in single-layer MoS2 by Vinod K. Sangwan, Deep Jariwala, In Soo Kim, Kan-Sheng Chen, Tobin J. Marks, Lincoln J. Lauhon, & Mark C. Hersam. Nature Nanotechnology (2015) doi:10.1038/nnano.2015.56 Published online 06 April 2015

This paper is behind a paywall but there is a few preview available through ReadCube Access.

Dexter Johnson has written about this latest memristor development in an April 9, 2015 posting on his Nanoclast blog (on the IEEE [Institute for Electrical and Electronics Engineers] website) where he notes this (Note: A link has been removed),

The memristor seems to generate fairly polarized debate, especially here on this website in the comments on stories covering the technology. The controversy seems to fall along the lines that the device that HP Labs’ Stan Williams and Greg Snider developed back in 2008 doesn’t exactly line up with the original theory of the memristor proposed by Leon Chua back in 1971.

It seems the ‘debate’ has evolved from issues about how the memristor is categorized. I wonder if there’s still discussion about whether or not HP Labs is attempting to develop a patent thicket of sorts.

Brain-like computing with optical fibres

Researchers from Singapore and the United Kingdom are exploring an optical fibre approach to brain-like computing (aka neuromorphic computing) as opposed to approaches featuring a memristor or other devices such as a nanoionic device that I’ve written about previously. A March 10, 2015 news item on Nanowerk describes this new approach,

Computers that function like the human brain could soon become a reality thanks to new research using optical fibres made of speciality glass.

Researchers from the Optoelectronics Research Centre (ORC) at the University of Southampton, UK, and Centre for Disruptive Photonic Technologies (CDPT) at the Nanyang Technological University (NTU), Singapore, have demonstrated how neural networks and synapses in the brain can be reproduced, with optical pulses as information carriers, using special fibres made from glasses that are sensitive to light, known as chalcogenides.

“The project, funded under Singapore’s Agency for Science, Technology and Research (A*STAR) Advanced Optics in Engineering programme, was conducted within The Photonics Institute (TPI), a recently established dual institute between NTU and the ORC.”

A March 10, 2015 University of Southampton press release (also on EurekAlert), which originated the news item, describes the nature of the problem that the scientists are trying address (Note: A link has been removed),

Co-author Professor Dan Hewak from the ORC, says: “Since the dawn of the computer age, scientists have sought ways to mimic the behaviour of the human brain, replacing neurons and our nervous system with electronic switches and memory. Now instead of electrons, light and optical fibres also show promise in achieving a brain-like computer. The cognitive functionality of central neurons underlies the adaptable nature and information processing capability of our brains.”

In the last decade, neuromorphic computing research has advanced software and electronic hardware that mimic brain functions and signal protocols, aimed at improving the efficiency and adaptability of conventional computers.

However, compared to our biological systems, today’s computers are more than a million times less efficient. Simulating five seconds of brain activity takes 500 seconds and needs 1.4 MW of power, compared to the small number of calories burned by the human brain.

Using conventional fibre drawing techniques, microfibers can be produced from chalcogenide (glasses based on sulphur) that possess a variety of broadband photoinduced effects, which allow the fibres to be switched on and off. This optical switching or light switching light, can be exploited for a variety of next generation computing applications capable of processing vast amounts of data in a much more energy-efficient manner.

Co-author Dr Behrad Gholipour explains: “By going back to biological systems for inspiration and using mass-manufacturable photonic platforms, such as chalcogenide fibres, we can start to improve the speed and efficiency of conventional computing architectures, while introducing adaptability and learning into the next generation of devices.”

By exploiting the material properties of the chalcogenides fibres, the team led by Professor Cesare Soci at NTU have demonstrated a range of optical equivalents of brain functions. These include holding a neural resting state and simulating the changes in electrical activity in a nerve cell as it is stimulated. In the proposed optical version of this brain function, the changing properties of the glass act as the varying electrical activity in a nerve cell, and light provides the stimulus to change these properties. This enables switching of a light signal, which is the equivalent to a nerve cell firing.

The research paves the way for scalable brain-like computing systems that enable ‘photonic neurons’ with ultrafast signal transmission speeds, higher bandwidth and lower power consumption than their biological and electronic counterparts.

Professor Cesare Soci said: “This work implies that ‘cognitive’ photonic devices and networks can be effectively used to develop non-Boolean computing and decision-making paradigms that mimic brain functionalities and signal protocols, to overcome bandwidth and power bottlenecks of traditional data processing.”

Here’s a link to and a citation for the paper,

Amorphous Metal-Sulphide Microfibers Enable Photonic Synapses for Brain-Like Computing by Behrad Gholipour, Paul Bastock, Chris Craig, Khouler Khan, Dan Hewak. and Cesare Soci. Advanced Optical Materials DOI: 10.1002/adom.201400472
Article first published online: 15 JAN 2015

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This article is behind a paywall.

For anyone interested in memristors and nanoionic devices, here are a few posts (from this blog) to get you started:

Memristors, memcapacitors, and meminductors for faster computers (June 30, 2014)

This second one offers more details and links to previous pieces,

Memristor, memristor! What is happening? News from the University of Michigan and HP Laboratories (June 25, 2014)

This post is more of a survey including memristors, nanoionic devices, ‘brain jelly, and more,

Brain-on-a-chip 2014 survey/overview (April 7, 2014)

One comment, this brain-on-a-chip is not to be confused with ‘organs-on-a-chip’ projects which are attempting to simulate human organs (Including the brain) so chemicals and drugs can be tested.